A Kalman Filter model is a State Space Transform of a Transfer Function model. It has been found to be useful when you have chunks of missing data and can incorporate transience in parameters. Strictly speaking, the models are similar…"
Assuming models is what has the world in a heap of trouble. Most software systems pick 30 models from a list and that is what you get. If you got a Rx for your glasses from a list of 30, would you be happy?
We have a tool…"
Why do you say you can't forecast all 52k??? Of course, you can.
Yes, classic forecasting methods work here. I would recommend 3+ years of daily data and then roll up to a monthly level as day of the week and week of the year…"
Those approaches listed above suffer from not addressing outliers like "pulse", "level shift", "local time trends", "seasonal pulses" in each of the time series being modeled. Without validating that…"
If I understand you correctly, you should consider using a time trend variable.
Our software, Autobox, will automatically look for time trends to adapt to the data. This is done using "transfer function modeling" which as you may…"
"You should be using a Transfer Function model built for each store. With weekly data you can bring in 51 dummies for the seasonal effects. SAS can't do this for you automatically nor does it automatically adjust for outliers(trends, level…"
"Why would you want to impose a model to your data? Plus you are ignoring any outliers in your data which would negatively impact your coefficients. Simple methods like this don't work. Plus do you know of any causal variables that drive the…"